docs fix

docs/examples/paper_implementations.md

@@ -47,10 +47,9 @@ We invite you to explore these implementations, contribute to our research effor
 | Paper Name | Description | Original Paper | Implementation | Status | Key Features |
 |------------|-------------|----------------|----------------|--------|--------------|
 | **MALT (Multi-Agent Learning Task)** | A sophisticated orchestration framework that coordinates multiple specialized AI agents to tackle complex tasks through structured conversations. | [arXiv:2412.01928](https://arxiv.org/pdf/2412.01928) | [`swarms.structs.malt`](https://docs.swarms.world/en/latest/swarms/structs/malt/) | ✅ Complete | Creator-Verifier-Refiner architecture, structured conversations, reliability guarantees |
-| **MAI-DxO (MAI Diagnostic Orchestrator)** | An open-source implementation of Microsoft Research's "Sequential Diagnosis with Language Models" paper, simulating a virtual panel of physician-agents for iterative medical diagnosis. | Microsoft Research Paper | [GitHub Repository](https://github.com/The-Swarm-Corporation/Open-MAI-Dx-Orchestrator) | ✅ Complete | Cost-effective medical diagnosis, physician-agent panel, iterative refinement |
-| **AI-CoScientist** | A multi-agent AI framework for collaborative scientific research, implementing the "Towards an AI Co-Scientist" methodology with tournament-based hypothesis evolution. | "Towards an AI Co-Scientist" Paper | [GitHub Repository](https://github.com/The-Swarm-Corporation/AI-CoScientist) | ✅ Complete | Tournament-based selection, peer review systems, hypothesis evolution, Elo rating system |
-| **Mixture of Agents (MoA)** | A sophisticated multi-agent architecture that implements parallel processing with iterative refinement, combining diverse expert agents for comprehensive analysis. | Multi-agent collaboration concepts | [`swarms.structs.moa`](https://docs.swarms.world/en/latest/swarms/structs/moa/) | ✅ Complete | Parallel processing, expert agent combination, iterative refinement, state-of-the-art performance |
-| **Open Scientist** | A multi-agent system for scientific research exploration using specialized agents for hypothesis generation, peer review, ranking, evolution, and meta-analysis. | Scientific research methodology | [`examples/demos/open_scientist.py`](https://github.com/kyegomez/swarms/blob/main/examples/demos/open_scientist.py) | ✅ Complete | Hypothesis generation, peer review, ranking, evolution, meta-analysis, proximity control |
+| **[MAI-DxO (MAI Diagnostic Orchestrator)](https://arxiv.org/abs/2506.22405)** | An open-source implementation of Microsoft Research's "[Sequential Diagnosis with Language Models](https://arxiv.org/abs/2506.22405)" paper, simulating a virtual panel of physician-agents for iterative medical diagnosis. | Microsoft Research Paper | [GitHub Repository](https://github.com/The-Swarm-Corporation/Open-MAI-Dx-Orchestrator) | ✅ Complete | Cost-effective medical diagnosis, physician-agent panel, iterative refinement |
+| **[AI-CoScientist](https://storage.googleapis.com/coscientist_paper/ai_coscientist.pdf)** | A multi-agent AI framework for collaborative scientific research, implementing the "Towards an AI Co-Scientist" methodology with tournament-based hypothesis evolution. | "Towards an AI Co-Scientist" Paper | [GitHub Repository](https://github.com/The-Swarm-Corporation/AI-CoScientist) | ✅ Complete | Tournament-based selection, peer review systems, hypothesis evolution, Elo rating system |
+| **[Mixture of Agents (MoA)](https://arxiv.org/abs/2406.04692)** | A sophisticated multi-agent architecture that implements parallel processing with iterative refinement, combining diverse expert agents for comprehensive analysis. | Multi-agent collaboration concepts | [`swarms.structs.moa`](https://docs.swarms.world/en/latest/swarms/structs/moa/) | ✅ Complete | Parallel processing, expert agent combination, iterative refinement, state-of-the-art performance |
 | **Deep Research Swarm** | A production-grade research system that conducts comprehensive analysis across multiple domains using parallel processing and advanced AI agents. | Research methodology | [`swarms.structs.deep_research_swarm`](https://docs.swarms.world/en/latest/swarms/structs/deep_research_swarm/) | ✅ Complete | Parallel search processing, multi-agent coordination, information synthesis, concurrent execution |
 | **Agent-as-a-Judge** | An evaluation framework that uses agents to evaluate other agents, implementing the "Agent-as-a-Judge: Evaluate Agents with Agents" methodology. | [arXiv:2410.10934](https://arxiv.org/abs/2410.10934) | [`swarms.agents.agent_judge`](https://docs.swarms.world/en/latest/swarms/agents/agent_judge/) | ✅ Complete | Agent evaluation, quality assessment, automated judging, performance metrics |
 

scripts/examples.py

@@ -1,5 +1,6 @@
 import requests
 
+
 def get_example_py_urls():
     owner = "kyegomez"
     repo = "swarms"
@@ -7,7 +8,9 @@ def get_example_py_urls():
     examples_path = "examples"
 
     api_url = f"https://api.github.com/repos/{owner}/{repo}/git/trees/{branch}?recursive=1"
-    raw_base = f"https://raw.githubusercontent.com/{owner}/{repo}/{branch}/"
+    raw_base = (
+        f"https://raw.githubusercontent.com/{owner}/{repo}/{branch}/"
+    )
 
     response = requests.get(api_url)
     response.raise_for_status()
@@ -25,6 +28,7 @@ def get_example_py_urls():
 
     return example_files
 
+
 if __name__ == "__main__":
     urls = get_example_py_urls()
     for url in urls:

swarms/structs/hiearchical_swarm.py

@@ -11,7 +11,7 @@ Flow:
 """
 
 import traceback
-from typing import Any, List, Literal, Optional, Union, Callable
+from typing import Any, Callable, List, Literal, Optional, Union
 
 from pydantic import BaseModel, Field
 
@@ -21,15 +21,13 @@ from swarms.prompts.hiearchical_system_prompt import (
 from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import BaseSwarm
 from swarms.structs.conversation import Conversation
+from swarms.structs.ma_utils import list_all_agents
+from swarms.tools.base_tool import BaseTool
 from swarms.utils.history_output_formatter import (
     history_output_formatter,
 )
-from swarms.utils.output_types import OutputType
-
 from swarms.utils.loguru_logger import initialize_logger
-from swarms.tools.base_tool import BaseTool
-from swarms.structs.ma_utils import list_all_agents
-
+from swarms.utils.output_types import OutputType
 
 logger = initialize_logger(log_folder="hierarchical_swarm")
 
@@ -118,6 +116,7 @@ class HierarchicalSwarm(BaseSwarm):
         planning_director_agent: Optional[
             Union[Agent, Callable, Any]
         ] = None,
+        director_feedback_on: bool = True,
         *args,
         **kwargs,
     ):
@@ -150,6 +149,7 @@ class HierarchicalSwarm(BaseSwarm):
         self.director_model_name = director_model_name
         self.add_collaboration_prompt = add_collaboration_prompt
         self.planning_director_agent = planning_director_agent
+        self.director_feedback_on = director_feedback_on
 
         self.init_swarm()
 
@@ -354,7 +354,10 @@ class HierarchicalSwarm(BaseSwarm):
             if self.verbose:
                 logger.info(f"⚡ Executed {len(outputs)} orders")
 
-            feedback = self.feedback_director(outputs)
+            if self.director_feedback_on is True:
+                feedback = self.feedback_director(outputs)
+            else:
+                feedback = outputs
 
             if self.verbose:
                 logger.success("✅ Step completed successfully")
@@ -469,7 +472,9 @@ class HierarchicalSwarm(BaseSwarm):
                     f"Worker Agent Responses: {task}"
                 )
             )
-            self.conversation.add(role="Director", content=output)
+            self.conversation.add(
+                role=self.director.agent_name, content=output
+            )
 
             if self.verbose:
                 logger.success(